Overview

Dataset statistics

Number of variables12
Number of observations7779
Missing cells3404
Missing cells (%)3.6%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory729.4 KiB
Average record size in memory96.0 B

Variable types

Numeric9
Categorical3

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
Comuna has a high cardinality: 51 distinct valuesHigh cardinality
Ubicacion has a high cardinality: 6620 distinct valuesHigh cardinality
Realtor has a high cardinality: 278 distinct valuesHigh cardinality
Price_CLP is highly overall correlated with Price_UF and 6 other fieldsHigh correlation
Price_UF is highly overall correlated with Price_CLP and 6 other fieldsHigh correlation
Price_USD is highly overall correlated with Price_CLP and 6 other fieldsHigh correlation
Dorms is highly overall correlated with Price_CLP and 5 other fieldsHigh correlation
Baths is highly overall correlated with Price_CLP and 5 other fieldsHigh correlation
Built Area is highly overall correlated with Price_CLP and 6 other fieldsHigh correlation
Total Area is highly overall correlated with Price_CLP and 6 other fieldsHigh correlation
Parking is highly overall correlated with Price_CLP and 4 other fieldsHigh correlation
Built Area has 246 (3.2%) missing valuesMissing
Total Area has 208 (2.7%) missing valuesMissing
Parking has 2290 (29.4%) missing valuesMissing
Realtor has 595 (7.6%) missing valuesMissing
Built Area is highly skewed (γ1 = 55.99567709)Skewed
Total Area is highly skewed (γ1 = 61.26597193)Skewed
Parking is highly skewed (γ1 = 67.07753911)Skewed

Reproduction

Analysis started2023-05-01 01:14:38.124042
Analysis finished2023-05-01 01:14:56.936964
Duration18.81 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

Price_CLP
Real number (ℝ)

Distinct1897
Distinct (%)24.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6424812 × 108
Minimum2085
Maximum5.51645 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:57.152355image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum2085
5-th percentile65000000
Q11.2 × 108
median2.05 × 108
Q34.91142 × 108
95-th percentile1.1000869 × 109
Maximum5.51645 × 109
Range5.5164479 × 109
Interquartile range (IQR)3.71142 × 108

Descriptive statistics

Standard deviation3.8688099 × 108
Coefficient of variation (CV)1.0621359
Kurtosis14.312728
Mean3.6424812 × 108
Median Absolute Deviation (MAD)1.2 × 108
Skewness2.8003943
Sum2.8334861 × 1012
Variance1.496769 × 1017
MonotonicityNot monotonic
2023-04-30T21:14:57.356479image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120000000 104
 
1.3%
85000000 91
 
1.2%
130000000 85
 
1.1%
150000000 83
 
1.1%
125000000 77
 
1.0%
75000000 74
 
1.0%
140000000 72
 
0.9%
110000000 69
 
0.9%
135000000 68
 
0.9%
80000000 63
 
0.8%
Other values (1887) 6993
89.9%
ValueCountFrequency (%)
2085 1
< 0.1%
3171 1
< 0.1%
3490 1
< 0.1%
4100 1
< 0.1%
4350 1
< 0.1%
4520 1
< 0.1%
4780 1
< 0.1%
4800 1
< 0.1%
5836 1
< 0.1%
6800 1
< 0.1%
ValueCountFrequency (%)
5516450000 1
 
< 0.1%
4804650000 1
 
< 0.1%
3736950000 1
 
< 0.1%
3558288200 1
 
< 0.1%
3452230000 1
 
< 0.1%
3381050000 1
 
< 0.1%
3203100000 2
< 0.1%
3131920000 1
 
< 0.1%
2847200000 3
< 0.1%
2800000000 1
 
< 0.1%

Price_UF
Real number (ℝ)

Distinct1801
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10234.571
Minimum0
Maximum155000
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:57.579350image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1826
Q13372
median5760
Q313800
95-th percentile30910
Maximum155000
Range155000
Interquartile range (IQR)10428

Descriptive statistics

Standard deviation10870.492
Coefficient of variation (CV)1.0621345
Kurtosis14.312754
Mean10234.571
Median Absolute Deviation (MAD)3372
Skewness2.8003968
Sum79614729
Variance1.1816759 × 108
MonotonicityNot monotonic
2023-04-30T21:14:57.791992image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3372 104
 
1.3%
2388 92
 
1.2%
3653 87
 
1.1%
4215 83
 
1.1%
3512 78
 
1.0%
2107 75
 
1.0%
3934 72
 
0.9%
3793 70
 
0.9%
3091 69
 
0.9%
2248 64
 
0.8%
Other values (1791) 6985
89.8%
ValueCountFrequency (%)
0 18
0.2%
1 1
 
< 0.1%
40 4
 
0.1%
41 1
 
< 0.1%
152 2
 
< 0.1%
210 1
 
< 0.1%
295 1
 
< 0.1%
300 1
 
< 0.1%
309 1
 
< 0.1%
407 1
 
< 0.1%
ValueCountFrequency (%)
155000 1
 
< 0.1%
135000 1
 
< 0.1%
105000 1
 
< 0.1%
99980 1
 
< 0.1%
97000 1
 
< 0.1%
95000 1
 
< 0.1%
90000 2
< 0.1%
88000 1
 
< 0.1%
80000 3
< 0.1%
78674 1
 
< 0.1%

Price_USD
Real number (ℝ)

Distinct1877
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean453609.11
Minimum3
Maximum6869801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:58.039395image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile80946
Q1149440
median255293
Q3611634
95-th percentile1369971.2
Maximum6869801
Range6869798
Interquartile range (IQR)462194

Descriptive statistics

Standard deviation481794.5
Coefficient of variation (CV)1.0621359
Kurtosis14.312729
Mean453609.11
Median Absolute Deviation (MAD)149440
Skewness2.8003943
Sum3.5286253 × 109
Variance2.3212594 × 1011
MonotonicityNot monotonic
2023-04-30T21:14:58.265633image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
149440 104
 
1.3%
105853 91
 
1.2%
161893 86
 
1.1%
186800 83
 
1.1%
155666 77
 
1.0%
93400 74
 
1.0%
174346 72
 
0.9%
136986 69
 
0.9%
168120 68
 
0.9%
99626 63
 
0.8%
Other values (1867) 6992
89.9%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 2
< 0.1%
5 2
< 0.1%
6 3
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
10 3
< 0.1%
11 1
 
< 0.1%
12 2
< 0.1%
14 2
< 0.1%
ValueCountFrequency (%)
6869801 1
 
< 0.1%
5983375 1
 
< 0.1%
4653736 1
 
< 0.1%
4431243 1
 
< 0.1%
4299166 1
 
< 0.1%
4210523 1
 
< 0.1%
3988917 2
< 0.1%
3900274 1
 
< 0.1%
3545704 3
< 0.1%
3486924 1
 
< 0.1%

Comuna
Categorical

Distinct51
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
PuenteAlto
814 
LasCondes
638 
Maipú
630 
Colina
553 
LaFlorida
 
467
Other values (46)
4677 

Length

Max length17
Median length14
Mean length8.4752539
Min length4

Characters and Unicode

Total characters65929
Distinct characters45
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowQuintaNormal
2nd rowPedroAguirreCerda
3rd rowEstaciónCentral
4th rowColina
5th rowColina

Common Values

ValueCountFrequency (%)
PuenteAlto 814
 
10.5%
LasCondes 638
 
8.2%
Maipú 630
 
8.1%
Colina 553
 
7.1%
LaFlorida 467
 
6.0%
LoBarnechea 455
 
5.8%
SanBernardo 309
 
4.0%
Peñalolén 284
 
3.7%
Santiago 279
 
3.6%
LaReina 253
 
3.3%
Other values (41) 3097
39.8%

Length

2023-04-30T21:14:58.486205image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
puentealto 814
 
10.5%
lascondes 638
 
8.2%
maipú 630
 
8.1%
colina 553
 
7.1%
laflorida 467
 
6.0%
lobarnechea 455
 
5.8%
sanbernardo 309
 
4.0%
peñalolén 284
 
3.7%
santiago 279
 
3.6%
lareina 253
 
3.3%
Other values (41) 3097
39.8%

Most occurring characters

ValueCountFrequency (%)
a 9830
14.9%
e 6045
 
9.2%
n 5453
 
8.3%
o 5140
 
7.8%
i 4076
 
6.2%
l 3837
 
5.8%
r 3377
 
5.1%
u 2914
 
4.4%
t 2848
 
4.3%
d 2265
 
3.4%
Other values (35) 20144
30.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 54156
82.1%
Uppercase Letter 11773
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 9830
18.2%
e 6045
11.2%
n 5453
10.1%
o 5140
9.5%
i 4076
7.5%
l 3837
 
7.1%
r 3377
 
6.2%
u 2914
 
5.4%
t 2848
 
5.3%
d 2265
 
4.2%
Other values (16) 8371
15.5%
Uppercase Letter
ValueCountFrequency (%)
L 2257
19.2%
P 1818
15.4%
C 1638
13.9%
B 980
8.3%
M 925
7.9%
A 863
 
7.3%
S 783
 
6.7%
F 467
 
4.0%
R 424
 
3.6%
E 246
 
2.1%
Other values (9) 1372
11.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 65929
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 9830
14.9%
e 6045
 
9.2%
n 5453
 
8.3%
o 5140
 
7.8%
i 4076
 
6.2%
l 3837
 
5.8%
r 3377
 
5.1%
u 2914
 
4.4%
t 2848
 
4.3%
d 2265
 
3.4%
Other values (35) 20144
30.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63927
97.0%
None 2002
 
3.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 9830
15.4%
e 6045
 
9.5%
n 5453
 
8.5%
o 5140
 
8.0%
i 4076
 
6.4%
l 3837
 
6.0%
r 3377
 
5.3%
u 2914
 
4.6%
t 2848
 
4.5%
d 2265
 
3.5%
Other values (29) 18142
28.4%
None
ValueCountFrequency (%)
ú 630
31.5%
ñ 595
29.7%
é 296
14.8%
Ñ 222
 
11.1%
í 133
 
6.6%
ó 126
 
6.3%

Ubicacion
Categorical

Distinct6620
Distinct (%)85.1%
Missing0
Missing (%)0.0%
Memory size60.9 KiB
Maipú
 
45
PuenteAlto
 
39
Noespecifica
 
20
Colina
 
19
LaFlorida
 
17
Other values (6615)
7639 

Length

Max length114
Median length68
Mean length20.363029
Min length2

Characters and Unicode

Total characters158404
Distinct characters100
Distinct categories14 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6021 ?
Unique (%)77.4%

Sample

1st rowHoevel4548y4558
2nd rowRucalhue
3rd rowAvenidaLasParcelas
4th rowPasajeGonzaloRojas
5th rowHernánDíazArrieta2820

Common Values

ValueCountFrequency (%)
Maipú 45
 
0.6%
PuenteAlto 39
 
0.5%
Noespecifica 20
 
0.3%
Colina 19
 
0.2%
LaFlorida 17
 
0.2%
EstanciaLiray 16
 
0.2%
Venta 16
 
0.2%
SanBernardo 16
 
0.2%
PiedraRoja 16
 
0.2%
Altomacul 14
 
0.2%
Other values (6610) 7561
97.2%

Length

2023-04-30T21:14:58.902909image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
maipú 45
 
0.6%
puentealto 45
 
0.6%
laflorida 25
 
0.3%
piedraroja 22
 
0.3%
noespecifica 20
 
0.3%
colina 19
 
0.2%
sanbernardo 18
 
0.2%
estancialiray 17
 
0.2%
venta 16
 
0.2%
altomacul 16
 
0.2%
Other values (6357) 7536
96.9%

Most occurring characters

ValueCountFrequency (%)
a 21091
13.3%
e 14336
 
9.1%
o 12799
 
8.1%
n 10497
 
6.6%
r 10054
 
6.3%
i 9420
 
5.9%
l 8879
 
5.6%
s 7833
 
4.9%
d 5491
 
3.5%
t 5330
 
3.4%
Other values (90) 52674
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 131708
83.1%
Uppercase Letter 17867
 
11.3%
Decimal Number 5298
 
3.3%
Other Punctuation 2912
 
1.8%
Dash Punctuation 495
 
0.3%
Math Symbol 57
 
< 0.1%
Open Punctuation 22
 
< 0.1%
Close Punctuation 21
 
< 0.1%
Modifier Symbol 10
 
< 0.1%
Other Symbol 7
 
< 0.1%
Other values (4) 7
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 21091
16.0%
e 14336
10.9%
o 12799
9.7%
n 10497
8.0%
r 10054
 
7.6%
i 9420
 
7.2%
l 8879
 
6.7%
s 7833
 
5.9%
d 5491
 
4.2%
t 5330
 
4.0%
Other values (27) 25978
19.7%
Uppercase Letter
ValueCountFrequency (%)
C 2757
15.4%
A 1932
10.8%
L 1910
10.7%
P 1635
9.2%
M 1297
 
7.3%
S 1255
 
7.0%
E 1002
 
5.6%
V 854
 
4.8%
R 652
 
3.6%
B 634
 
3.5%
Other values (19) 3939
22.0%
Other Punctuation
ValueCountFrequency (%)
/ 1894
65.0%
. 823
28.3%
! 95
 
3.3%
& 55
 
1.9%
¡ 25
 
0.9%
# 6
 
0.2%
" 4
 
0.1%
' 4
 
0.1%
¿ 2
 
0.1%
: 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 824
15.6%
1 790
14.9%
2 614
11.6%
5 553
10.4%
3 533
10.1%
6 470
8.9%
4 437
8.2%
7 383
7.2%
9 357
6.7%
8 337
6.4%
Modifier Symbol
ValueCountFrequency (%)
´ 7
70.0%
¨ 2
 
20.0%
` 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
+ 55
96.5%
| 2
 
3.5%
Dash Punctuation
ValueCountFrequency (%)
- 495
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Other Symbol
ValueCountFrequency (%)
° 7
100.0%
Other Letter
ValueCountFrequency (%)
º 3
100.0%
Final Punctuation
ValueCountFrequency (%)
2
100.0%
Control
ValueCountFrequency (%)
‰ 1
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 149578
94.4%
Common 8826
 
5.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 21091
14.1%
e 14336
 
9.6%
o 12799
 
8.6%
n 10497
 
7.0%
r 10054
 
6.7%
i 9420
 
6.3%
l 8879
 
5.9%
s 7833
 
5.2%
d 5491
 
3.7%
t 5330
 
3.6%
Other values (57) 43848
29.3%
Common
ValueCountFrequency (%)
/ 1894
21.5%
0 824
9.3%
. 823
9.3%
1 790
9.0%
2 614
 
7.0%
5 553
 
6.3%
3 533
 
6.0%
- 495
 
5.6%
6 470
 
5.3%
4 437
 
5.0%
Other values (23) 1393
15.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 156672
98.9%
None 1730
 
1.1%
Punctuation 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 21091
13.5%
e 14336
 
9.2%
o 12799
 
8.2%
n 10497
 
6.7%
r 10054
 
6.4%
i 9420
 
6.0%
l 8879
 
5.7%
s 7833
 
5.0%
d 5491
 
3.5%
t 5330
 
3.4%
Other values (68) 50942
32.5%
None
ValueCountFrequency (%)
ñ 420
24.3%
í 355
20.5%
é 285
16.5%
ó 262
15.1%
á 191
11.0%
ú 130
 
7.5%
¡ 25
 
1.4%
Ñ 19
 
1.1%
Á 15
 
0.9%
° 7
 
0.4%
Other values (11) 21
 
1.2%
Punctuation
ValueCountFrequency (%)
2
100.0%

Dorms
Real number (ℝ)

Distinct22
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9940866
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:59.095925image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7
Maximum27
Range26
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.6228207
Coefficient of variation (CV)0.40630583
Kurtosis23.312355
Mean3.9940866
Median Absolute Deviation (MAD)1
Skewness3.0231931
Sum31070
Variance2.6335469
MonotonicityNot monotonic
2023-04-30T21:14:59.277074image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
3 2764
35.5%
4 2159
27.8%
5 1245
16.0%
2 632
 
8.1%
6 504
 
6.5%
7 202
 
2.6%
8 93
 
1.2%
1 55
 
0.7%
10 40
 
0.5%
9 37
 
0.5%
Other values (12) 48
 
0.6%
ValueCountFrequency (%)
1 55
 
0.7%
2 632
 
8.1%
3 2764
35.5%
4 2159
27.8%
5 1245
16.0%
6 504
 
6.5%
7 202
 
2.6%
8 93
 
1.2%
9 37
 
0.5%
10 40
 
0.5%
ValueCountFrequency (%)
27 1
 
< 0.1%
25 2
 
< 0.1%
24 1
 
< 0.1%
20 1
 
< 0.1%
18 1
 
< 0.1%
17 2
 
< 0.1%
16 2
 
< 0.1%
15 6
0.1%
14 5
0.1%
13 6
0.1%

Baths
Real number (ℝ)

Distinct15
Distinct (%)0.2%
Missing65
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean2.6537464
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:59.439152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median2
Q33
95-th percentile5
Maximum29
Range28
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.4651028
Coefficient of variation (CV)0.55208847
Kurtosis20.753874
Mean2.6537464
Median Absolute Deviation (MAD)1
Skewness2.2556166
Sum20471
Variance2.1465262
MonotonicityNot monotonic
2023-04-30T21:14:59.589005image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2 2289
29.4%
3 1963
25.2%
1 1701
21.9%
4 1029
13.2%
5 461
 
5.9%
6 153
 
2.0%
7 75
 
1.0%
8 23
 
0.3%
9 7
 
0.1%
15 4
 
0.1%
Other values (5) 9
 
0.1%
(Missing) 65
 
0.8%
ValueCountFrequency (%)
1 1701
21.9%
2 2289
29.4%
3 1963
25.2%
4 1029
13.2%
5 461
 
5.9%
6 153
 
2.0%
7 75
 
1.0%
8 23
 
0.3%
9 7
 
0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
29 1
 
< 0.1%
21 1
 
< 0.1%
15 4
 
0.1%
13 1
 
< 0.1%
12 3
 
< 0.1%
11 3
 
< 0.1%
9 7
 
0.1%
8 23
 
0.3%
7 75
1.0%
6 153
2.0%

Built Area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct573
Distinct (%)7.6%
Missing246
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean229.92367
Minimum1
Maximum120000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:14:59.789437image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile54
Q185
median128
Q3200
95-th percentile450
Maximum120000
Range119999
Interquartile range (IQR)115

Descriptive statistics

Standard deviation1676.8988
Coefficient of variation (CV)7.2932848
Kurtosis3692.3835
Mean229.92367
Median Absolute Deviation (MAD)50
Skewness55.995677
Sum1732015
Variance2811989.6
MonotonicityNot monotonic
2023-04-30T21:15:00.028532image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
140 274
 
3.5%
90 218
 
2.8%
100 203
 
2.6%
80 198
 
2.5%
120 192
 
2.5%
70 173
 
2.2%
110 126
 
1.6%
180 125
 
1.6%
200 125
 
1.6%
60 115
 
1.5%
Other values (563) 5784
74.4%
(Missing) 246
 
3.2%
ValueCountFrequency (%)
1 6
0.1%
4 1
 
< 0.1%
15 1
 
< 0.1%
18 2
 
< 0.1%
20 1
 
< 0.1%
21 1
 
< 0.1%
25 1
 
< 0.1%
30 3
< 0.1%
31 1
 
< 0.1%
32 3
< 0.1%
ValueCountFrequency (%)
120000 1
< 0.1%
60000 1
< 0.1%
31972 1
< 0.1%
18917 1
< 0.1%
17525 1
< 0.1%
15951 1
< 0.1%
15190 1
< 0.1%
10875 1
< 0.1%
8000 1
< 0.1%
7035 1
< 0.1%

Total Area
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct1099
Distinct (%)14.5%
Missing208
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean807.91983
Minimum1
Maximum678000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:15:00.252842image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile74
Q1129.5
median210
Q3443
95-th percentile2800
Maximum678000
Range677999
Interquartile range (IQR)313.5

Descriptive statistics

Standard deviation9050.8931
Coefficient of variation (CV)11.202712
Kurtosis4314.0824
Mean807.91983
Median Absolute Deviation (MAD)109
Skewness61.265972
Sum6116761
Variance81918666
MonotonicityNot monotonic
2023-04-30T21:15:00.479832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 215
 
2.8%
120 172
 
2.2%
100 152
 
2.0%
300 132
 
1.7%
5000 126
 
1.6%
180 118
 
1.5%
80 115
 
1.5%
160 105
 
1.3%
90 101
 
1.3%
110 93
 
1.2%
Other values (1089) 6242
80.2%
(Missing) 208
 
2.7%
ValueCountFrequency (%)
1 6
0.1%
10 1
 
< 0.1%
12 1
 
< 0.1%
18 1
 
< 0.1%
20 2
 
< 0.1%
21 1
 
< 0.1%
25 1
 
< 0.1%
32 1
 
< 0.1%
34 1
 
< 0.1%
37 5
0.1%
ValueCountFrequency (%)
678000 1
< 0.1%
300000 1
< 0.1%
160000 1
< 0.1%
94929 1
< 0.1%
75000 2
< 0.1%
60000 1
< 0.1%
54000 1
< 0.1%
47800 1
< 0.1%
45500 1
< 0.1%
44000 1
< 0.1%

Parking
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED 

Distinct26
Distinct (%)0.5%
Missing2290
Missing (%)29.4%
Infinite0
Infinite (%)0.0%
Mean2.9805065
Minimum1
Maximum1269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:15:00.703792image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile7
Maximum1269
Range1268
Interquartile range (IQR)2

Descriptive statistics

Standard deviation17.749384
Coefficient of variation (CV)5.9551569
Kurtosis4734.326
Mean2.9805065
Median Absolute Deviation (MAD)1
Skewness67.077539
Sum16360
Variance315.04062
MonotonicityNot monotonic
2023-04-30T21:15:00.888697image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2 1908
24.5%
1 1630
21.0%
3 831
 
10.7%
4 446
 
5.7%
5 212
 
2.7%
6 186
 
2.4%
8 84
 
1.1%
10 53
 
0.7%
7 48
 
0.6%
12 18
 
0.2%
Other values (16) 73
 
0.9%
(Missing) 2290
29.4%
ValueCountFrequency (%)
1 1630
21.0%
2 1908
24.5%
3 831
10.7%
4 446
 
5.7%
5 212
 
2.7%
6 186
 
2.4%
7 48
 
0.6%
8 84
 
1.1%
9 9
 
0.1%
10 53
 
0.7%
ValueCountFrequency (%)
1269 1
 
< 0.1%
307 1
 
< 0.1%
60 1
 
< 0.1%
30 2
 
< 0.1%
23 2
 
< 0.1%
22 5
0.1%
20 9
0.1%
19 1
 
< 0.1%
18 2
 
< 0.1%
17 2
 
< 0.1%

id
Real number (ℝ)

Distinct7778
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9910827.7
Minimum1213620
Maximum12341490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.9 KiB
2023-04-30T21:15:01.108006image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1213620
5-th percentile6033050.8
Q18563078.5
median10548072
Q311524632
95-th percentile12239128
Maximum12341490
Range11127870
Interquartile range (IQR)2961553.5

Descriptive statistics

Standard deviation2046316.7
Coefficient of variation (CV)0.20647283
Kurtosis0.19829638
Mean9910827.7
Median Absolute Deviation (MAD)1276266
Skewness-0.93560409
Sum7.7096329 × 1010
Variance4.1874119 × 1012
MonotonicityNot monotonic
2023-04-30T21:15:01.331648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10215663 2
 
< 0.1%
9490434 1
 
< 0.1%
9679579 1
 
< 0.1%
12162131 1
 
< 0.1%
11072235 1
 
< 0.1%
11423079 1
 
< 0.1%
9290469 1
 
< 0.1%
11766500 1
 
< 0.1%
10564323 1
 
< 0.1%
7999064 1
 
< 0.1%
Other values (7768) 7768
99.9%
ValueCountFrequency (%)
1213620 1
< 0.1%
1367271 1
< 0.1%
1796413 1
< 0.1%
1933271 1
< 0.1%
2003240 1
< 0.1%
2234783 1
< 0.1%
2272898 1
< 0.1%
2355567 1
< 0.1%
2470109 1
< 0.1%
2526485 1
< 0.1%
ValueCountFrequency (%)
12341490 1
< 0.1%
12341157 1
< 0.1%
12341129 1
< 0.1%
12339449 1
< 0.1%
12337901 1
< 0.1%
12337835 1
< 0.1%
12337819 1
< 0.1%
12337777 1
< 0.1%
12337693 1
< 0.1%
12337577 1
< 0.1%

Realtor
Categorical

HIGH CARDINALITY  MISSING 

Distinct278
Distinct (%)3.9%
Missing595
Missing (%)7.6%
Memory size60.9 KiB
Unne
1013 
Nexxos
598 
Easyprop
 
448
Mi Llave
 
295
Propiedades Centro Santiago SPA
 
291
Other values (273)
4539 

Length

Max length50
Median length43
Mean length16.661052
Min length4

Characters and Unicode

Total characters119693
Distinct characters74
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)1.0%

Sample

1st rowLegales y Propiedades SpA
2nd rowPropiedadesrs
3rd rowPatricio Gajardo propiedades
4th rowPatricio Gajardo propiedades
5th rowPatricio Gajardo propiedades

Common Values

ValueCountFrequency (%)
Unne 1013
 
13.0%
Nexxos 598
 
7.7%
Easyprop 448
 
5.8%
Mi Llave 295
 
3.8%
Propiedades Centro Santiago SPA 291
 
3.7%
Houm 244
 
3.1%
Todo Propiedades 231
 
3.0%
Movahome Corredores Integrados 228
 
2.9%
Corredores Asociados - OpenBrokers 212
 
2.7%
Agente Propiedades 170
 
2.2%
Other values (268) 3454
44.4%
(Missing) 595
 
7.6%

Length

2023-04-30T21:15:01.571767image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
propiedades 2516
 
15.5%
unne 1013
 
6.2%
nexxos 598
 
3.7%
spa 598
 
3.7%
corredores 513
 
3.2%
easyprop 448
 
2.8%
santiago 436
 
2.7%
334
 
2.1%
mi 303
 
1.9%
llave 303
 
1.9%
Other values (412) 9192
56.6%

Most occurring characters

ValueCountFrequency (%)
e 12494
 
10.4%
o 10837
 
9.1%
a 10210
 
8.5%
9124
 
7.6%
r 8662
 
7.2%
d 7500
 
6.3%
i 6869
 
5.7%
s 6728
 
5.6%
n 5341
 
4.5%
p 4424
 
3.7%
Other values (64) 37504
31.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 90797
75.9%
Uppercase Letter 18636
 
15.6%
Space Separator 9124
 
7.6%
Other Punctuation 614
 
0.5%
Dash Punctuation 254
 
0.2%
Decimal Number 166
 
0.1%
Open Punctuation 49
 
< 0.1%
Close Punctuation 49
 
< 0.1%
Other Symbol 3
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 12494
13.8%
o 10837
11.9%
a 10210
11.2%
r 8662
9.5%
d 7500
8.3%
i 6869
7.6%
s 6728
7.4%
n 5341
 
5.9%
p 4424
 
4.9%
t 2834
 
3.1%
Other values (22) 14898
16.4%
Uppercase Letter
ValueCountFrequency (%)
P 3791
20.3%
A 1955
10.5%
S 1794
9.6%
C 1588
8.5%
U 1371
 
7.4%
M 943
 
5.1%
L 919
 
4.9%
N 854
 
4.6%
I 765
 
4.1%
E 737
 
4.0%
Other values (17) 3919
21.0%
Decimal Number
ValueCountFrequency (%)
0 70
42.2%
1 35
21.1%
3 20
 
12.0%
6 20
 
12.0%
5 20
 
12.0%
4 1
 
0.6%
Other Punctuation
ValueCountFrequency (%)
. 518
84.4%
& 86
 
14.0%
@ 10
 
1.6%
Space Separator
ValueCountFrequency (%)
9124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 254
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Other Symbol
ValueCountFrequency (%)
® 3
100.0%
Math Symbol
ValueCountFrequency (%)
| 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 109433
91.4%
Common 10260
 
8.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 12494
 
11.4%
o 10837
 
9.9%
a 10210
 
9.3%
r 8662
 
7.9%
d 7500
 
6.9%
i 6869
 
6.3%
s 6728
 
6.1%
n 5341
 
4.9%
p 4424
 
4.0%
P 3791
 
3.5%
Other values (49) 32577
29.8%
Common
ValueCountFrequency (%)
9124
88.9%
. 518
 
5.0%
- 254
 
2.5%
& 86
 
0.8%
0 70
 
0.7%
( 49
 
0.5%
) 49
 
0.5%
1 35
 
0.3%
3 20
 
0.2%
6 20
 
0.2%
Other values (5) 35
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119210
99.6%
None 483
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 12494
 
10.5%
o 10837
 
9.1%
a 10210
 
8.6%
9124
 
7.7%
r 8662
 
7.3%
d 7500
 
6.3%
i 6869
 
5.8%
s 6728
 
5.6%
n 5341
 
4.5%
p 4424
 
3.7%
Other values (55) 37021
31.1%
None
ValueCountFrequency (%)
í 175
36.2%
á 147
30.4%
ó 104
21.5%
Ó 16
 
3.3%
ú 12
 
2.5%
ñ 12
 
2.5%
é 8
 
1.7%
ü 6
 
1.2%
® 3
 
0.6%

Interactions

2023-04-30T21:14:54.218482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:39.778131image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-04-30T21:14:46.945275image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-04-30T21:14:52.411337image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:54.409068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-04-30T21:14:54.651194image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-04-30T21:14:49.090379image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:50.859270image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:52.813380image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:54.845845image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:40.372092image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:42.220810image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:44.097280image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:45.837474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:47.541295image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:49.306001image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:51.067016image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:53.013451image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:55.025766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:40.551652image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:42.413283image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:44.284501image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:46.014486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:47.728241image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:49.488380image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:51.428591image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:53.244034image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:55.213941image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:40.738050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:42.617309image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:44.480077image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:46.196486image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:47.913466image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:49.681169image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:51.625537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:53.438564image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:55.403069image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:40.927765image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:42.815385image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:44.674426image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:46.382646image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:48.112776image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:49.866997image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:51.818161image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:53.635342image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:55.600637image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:41.122644image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:43.024742image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:44.878730image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:46.575147image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:48.310303image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:50.064443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:52.015326image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:53.835585image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:55.782394image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:41.307152image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:43.258174image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:45.072246image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:46.766093image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:48.498211image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:50.259716image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:52.215263image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-04-30T21:14:54.031155image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-04-30T21:15:01.750656image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Price_CLPPrice_UFPrice_USDDormsBathsBuilt AreaTotal AreaParkingidComuna
Price_CLP1.0001.0001.0000.5300.7440.8130.8340.5840.0800.248
Price_UF1.0001.0001.0000.5300.7440.8130.8340.5840.0800.248
Price_USD1.0001.0001.0000.5300.7440.8130.8340.5840.0800.248
Dorms0.5300.5300.5301.0000.5960.6570.5020.3600.0280.145
Baths0.7440.7440.7440.5961.0000.7150.6290.4610.0790.212
Built Area0.8130.8130.8130.6570.7151.0000.7940.5400.0440.130
Total Area0.8340.8340.8340.5020.6290.7941.0000.6340.0610.149
Parking0.5840.5840.5840.3600.4610.5400.6341.0000.0940.050
id0.0800.0800.0800.0280.0790.0440.0610.0941.0000.070
Comuna0.2480.2480.2480.1450.2120.1300.1490.0500.0701.000

Missing values

2023-04-30T21:14:56.112484image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-30T21:14:56.469391image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-30T21:14:56.778794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Price_CLPPrice_UFPrice_USDComunaUbicacionDormsBathsBuilt AreaTotal AreaParkingidRealtor
040928500011500509695QuintaNormalHoevel4548y455874.0384.0732.03.011700213NaN
11050000002950130760PedroAguirreCerdaRucalhue21.0112.0145.01.010894299Legales y Propiedades SpA
21281240003600159557EstaciónCentralAvenidaLasParcelas31.059.0243.02.010257635Propiedadesrs
375000000210793400ColinaPasajeGonzaloRojas31.0103.073.01.09232092Patricio Gajardo propiedades
453000000148966002ColinaHernánDíazArrieta282021.057.067.01.07085397Patricio Gajardo propiedades
5940000002641117061EstaciónCentralAvenida5deAbril21.072.0131.0NaN7586371Patricio Gajardo propiedades
675000000210793400ColinaGabrielaMistral/PabloNeruda31.070.080.02.011688480Patricio Gajardo propiedades
71050000002950130760LaFloridaSanCristóbalTres21.050.0120.01.012229303Gesinprop Gestión Inmobiliaria
876000000213594645MaipúPasajeJosédeMoraleda31.072.072.01.012223374Gesinprop Gestión Inmobiliaria
976000000213594645MaipúMatías31.046.090.01.012221068Gesinprop Gestión Inmobiliaria
Price_CLPPrice_UFPrice_USDComunaUbicacionDormsBathsBuilt AreaTotal AreaParkingidRealtor
776945000000126456040LoEspejoAmericovespucio000032.040.050.01.07063716NaN
77702491300007000310249SantiagoAntilhue33.0NaNNaN2.06981371NaN
777170000000196787173EstaciónCentralPasajecahuache108042.0NaNNaN1.06954270NaN
7772850601002390105928SanBernardoGeneralUrrutia51.0120.076.01.06959129V & S Asesorias & Gestion Inmobiliaria
77731352420000380001684209LoBarnecheaCaminoLaGolondrina56.0500.0913.04.04708978Propiedades Viña Limitada ®
77742491300000700003102491LasCondesCalleSanJosédeLaSierra55.0600.01800.05.04708915Propiedades Viña Limitada ®
77752420120006800301385PeñalolénPasajeMarNegro42.0124.0200.01.06641660NaN
777637369500001050004653736LasCondesCaminoLasFlores/CaminoPiedraRoja57.0460.04925.08.06032811Tsi Property
777756944000016000709141LaPintanaLosCipreses/LosDuraznos42.0311.02011.01.05314376Tsi Property
77783558288209998443124TalaganteLucasPacheco/Balmaceda53.0225.0366.0NaN6186867Tsi Property

Duplicate rows

Most frequently occurring

Price_CLPPrice_UFPrice_USDComunaUbicacionDormsBathsBuilt AreaTotal AreaParkingidRealtor# duplicates
02669250007500332410TiltilSevendecasaconampliaparcelaentiltil43.0250.06000.01.010215663Movahome Corredores Integrados2